Update on Genomic Databases and Resources at the National Center for Biotechnology Information

Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping Review

BACKGROUND

An increasing body of literature highlights the integration of machine learning with genomic data in psychiatry, particularly for complex mental health disorders such as schizophrenia. These advanced techniques offer promising potential for uncovering various facets of these disorders. A comprehensive review of the current applications of machine learning in conjunction with genomic data within this context can significantly enhance our understanding of the current state of research and its future directions.

OBJECTIVE

This study aims to conduct a systematic scoping review of the use of machine learning algorithms with genomic data in the field of schizophrenia.

METHODS

To conduct a systematic scoping review, a search was performed in the electronic databases MEDLINE, Web of Science, PsycNet (PsycINFO), and Google Scholar from 2013 to 2024. Studies at the intersection of schizophrenia, genomic data, and machine learning were evaluated.

RESULTS

The literature search identified 2437 eligible articles after removing duplicates. Following abstract screening, 143 full-text articles were assessed, and 121 were subsequently excluded. Therefore, 21 studies were thoroughly assessed. Various machine learning algorithms were used in the identified studies, with support vector machines being the most common. The studies notably used genomic data to predict schizophrenia, identify schizophrenia features, discover drugs, classify schizophrenia amongst other mental health disorders, and predict the quality of life of patients.

CONCLUSIONS

Several high-quality studies were identified. Yet, the application of machine learning with genomic data in the context of schizophrenia remains limited. Future research is essential to further evaluate the portability of these models and to explore their potential clinical applications.

Multifactorial genetic control and magnesium levels govern the production of a Streptomyces antibiotic with unusual cell density dependence

Streptomyces bacteria are renowned both for their antibiotic production capabilities and for their cryptic metabolic potential. Their metabolic repertoire is subject to stringent genetic control, with many of the associated biosynthetic gene clusters being repressed by the conserved nucleoid-associated protein Lsr2. In an effort to stimulate new antibiotic production in wild Streptomyces isolates, we leveraged the activity of an Lsr2 knockdown construct and successfully enhanced antibiotic production in the wild Streptomyces isolate WAC07094. We determined that this new activity stemmed from increased levels of the angucycline-like family member saquayamycin. Saquayamycin has both antibiotic and anti-cancer activities, and intriguingly, beyond Lsr2-mediated repression, we found saquayamycin production was also suppressed at high density on solid or in liquid growth media; its levels were greatest in low-density cultures. This density-dependent control was exerted at the level of the cluster-situated regulatory gene sqnR and was mediated in part through the activity of the PhoRP two-component regulatory system, where deleting phoRP led to both constitutive antibiotic production and sqnR expression. This suggests that PhoP functions to repress the expression of sqnR at high cell density. We further discovered that magnesium supplementation could alleviate this density dependence, although its action was independent of PhoP. Finally, we revealed that the nitrogen-responsive regulators GlnR and AfsQ1 could relieve the repression exerted by Lsr2 and PhoP. Intriguingly, we found that this low density-dependent production of saquayamycin was not unique to WAC07094; saquayamycin production by another wild isolate also exhibited low-density activation, suggesting that this spatial control may serve an important ecological function in their native environments.IMPORTANCEStreptomyces specialized metabolic gene clusters are subject to complex regulation, and their products are frequently not observed under standard laboratory growth conditions. For the wild Streptomyces isolate WAC07094, production of the angucycline-family compound saquayamycin is subject to a unique constellation of control factors. Notably, it is produced primarily at low cell density, in contrast to the high cell density production typical of most antibiotics. This unusual density dependence is conserved in other saquayamycin producers and is driven by the pathway-specific regulator SqnR, whose expression is influenced by both nutritional and genetic elements. Collectively, this work provides new insights into an intricate regulatory system governing antibiotic production and indicates there may be benefits to including low-density cultures in antibiotic screening platforms.

How to deal with uncertainty in prenatal genomics: A systematic review of guidelines and policies

Exome sequencing (ES) enhanced the diagnostic yield of genetic testing, but has also increased the possibility of uncertain findings. Prenatal ES is increasingly being offered after a fetal abnormality is detected through ultrasound. It is important to know how to handle uncertainty in this particularly stressful period. This systematic review aimed to provide a comprehensive overview of guidelines available for addressing uncertainty related to prenatal chromosomal microarray (CMA) and ES. Ten uncertainty types associated with prenatal ES and CMA were identified and defined by an international multidisciplinary team. Medline (all) and Embase were systematically searched. Laboratory scientists, clinical geneticists, psychologists, and a fetal medicine specialist screened the papers and performed the data extraction. Nineteen papers were included. Recommendations generally emphasized the importance of trio analysis, clinical information, data sharing, validation and re-analysis, protocols, multidisciplinary teams, genetic counselling, whether to limit the possible scope of results, and when to report particular findings. This systematic review helps provide a vocabulary for uncertainties, and a compass to navigate uncertainties. Prenatal CMA and ES guidelines provide a strong starting point for determining how to handle uncertainty. Gaps in guidelines and recommendations were identified and discussed to provide direction for future research and policy making.

Organotypic Models in Drug Development "Tumor Models and Cancer Systems Biology for the Investigation of Anticancer Drugs and Resistance Development"

The landscape of cancer treatment has improved over the past decades, aiming to reduce systemic toxicity and enhance compatibility with the quality of life of the patient. However, at the therapeutic level, metastatic cancer remains hugely challenging, based on the almost inevitable emergence of therapy resistance. A small subpopulation of cells able to survive drug treatment termed the minimal residual disease may either harbor resistance-associated mutations or be phenotypically resistant, allowing them to regrow and become the dominant population in the therapy-resistant tumor. Characterization of the profile of minimal residual disease represents the key to the identification of resistance drivers that underpin cancer evolution. Therapeutic regimens must, therefore, be dynamic and tailored to take into account the emergence of resistance as tumors evolve within a complex microenvironment in vivo. This requires the adoption of new technologies based on the culture of cancer cells in ways that more accurately reflect the intratumor microenvironment, and their analysis using omics and system-based technologies to enable a new era in the diagnostics, classification, and treatment of many cancer types by applying the concept "from the cell plate to the patient." In this chapter, we will present and discuss 3D model building and use, and provide comprehensive information on new genomic techniques that are increasing our understanding of drug action and the emergence of resistance.

TIP_finder: An HPC Software to Detect Transposable Element Insertion Polymorphisms in Large Genomic Datasets

Transposable elements (TEs) are non-static genomic units capable of moving indistinctly from one chromosomal location to another. Their insertion polymorphisms may cause beneficial mutations, such as the creation of new gene function, or deleterious in eukaryotes, e.g., different types of cancer in humans. A particular type of TE called LTR-retrotransposons comprises almost 8% of the human genome. Among LTR retrotransposons, human endogenous retroviruses (HERVs) bear structural and functional similarities to retroviruses. Several tools allow the detection of transposon insertion polymorphisms (TIPs) but fail to efficiently analyze large genomes or large datasets. Here, we developed a computational tool, named TIP_finder, able to detect mobile element insertions in very large genomes, through high-performance computing (HPC) and parallel programming, using the inference of discordant read pair analysis. TIP_finder inputs are (i) short pair reads such as those obtained by Illumina, (ii) a chromosome-level reference genome sequence, and (iii) a database of consensus TE sequences. The HPC strategy we propose adds scalability and provides a useful tool to analyze huge genomic datasets in a decent running time. TIP_finder accelerates the detection of transposon insertion polymorphisms (TIPs) by up to 55 times in breast cancer datasets and 46 times in cancer-free datasets compared to the fastest available algorithms. TIP_finder applies a validated strategy to find TIPs, accelerates the process through HPC, and addresses the issues of runtime for large-scale analyses in the post-genomic era. TIP_finder version 1.0 is available at https://github.com/simonorozcoarias/TIP_finder.

Adenosine-to-Inosine RNA Editing in Mouse and Human Brain Proteomes

Proteogenomics is based on the use of customized genome or RNA sequencing databases for interrogation of shotgun proteomics data in search for proteome-level evidence of genome variations or RNA editing. In this work, the products of adenosine-to-inosine RNA editing in human and murine brain proteomes are identified using publicly available brain proteome LC-MS/MS datasets and an RNA editome database compiled from several sources. After filtering of false-positive results, 20 and 37 sites of editing in proteins belonging to 14 and 32 genes are identified for murine and human brain proteomes, respectively. Eight sites of editing identified with high spectral counts overlapped between human and mouse brain samples. Some of these sites have been previously reported using orthogonal methods, such as α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) glutamate receptors, CYFIP2, coatomer alpha. Also, differential editing between neurons and microglia is demonstrated in this work for some of the proteins from primary murine brain cell cultures. Because many edited sites are still not characterized functionally at the protein level, the results provide a necessary background for their further analysis in normal and diseased cells and tissues using targeted proteomic approaches.

Investigating Cell Signaling with Gene Expression Datasets

Modern molecular biology is a data- and computationally-intensive field with few instructional resources for introducing undergraduate students to the requisite skills and techniques for analyzing large data sets. This Lesson helps students: (i) build an understanding of the role of signal transduction in the control of gene expression; (ii) improve written scientific communication skills through engagement in literature searches, data analysis, and writing reports; and (iii) develop an awareness of the procedures and protocols for analyzing and making inferences from high-content quantitative molecular biology data. The Lesson is most suited to upper level biology courses because it requires foundational knowledge on cellular organization, protein structure and function, and the tenets of information flow from DNA to proteins. The first step lays the foundation for understanding cell signaling, which can be accomplished through assigned readings and presentations. In subsequent active learning sessions, data analysis is integrated with exercises that provide insight into the structure of scientific papers. The Lesson emphasizes the role of quantitative methods in research and helps students gain experience with functional genomics databases and data analysis, which are important skills for molecular biologists. Assessment is conducted through mini-reports designed to gauge students' perceptions of the purpose of each step, their awareness of the possible limitations of the methods utilized, and the ability to identify opportunities for further investigation. Summative assessment is conducted through a final report. The modules are suitable for complementing wet-laboratory experiments and can be adapted for different courses that use molecular biology data.

Comparative genomics of the major parasitic worms

Parasitic nematodes (roundworms) and platyhelminths (flatworms) cause debilitating chronic infections of humans and animals, decimate crop production and are a major impediment to socioeconomic development. Here we report a broad comparative study of 81 genomes of parasitic and non-parasitic worms. We have identified gene family births and hundreds of expanded gene families at key nodes in the phylogeny that are relevant to parasitism. Examples include gene families that modulate host immune responses, enable parasite migration though host tissues or allow the parasite to feed. We reveal extensive lineage-specific differences in core metabolism and protein families historically targeted for drug development. From an in silico screen, we have identified and prioritized new potential drug targets and compounds for testing. This comparative genomics resource provides a much-needed boost for the research community to understand and combat parasitic worms.

Pathogenesis of Proteus mirabilis Infection

Proteus mirabilis, a Gram-negative rod-shaped bacterium most noted for its swarming motility and urease activity, frequently causes catheter-associated urinary tract infections (CAUTIs) that are often polymicrobial. These infections may be accompanied by urolithiasis, the development of bladder or kidney stones due to alkalinization of urine from urease-catalyzed urea hydrolysis. Adherence of the bacterium to epithelial and catheter surfaces is mediated by 17 different fimbriae, most notably MR/P fimbriae. Repressors of motility are often encoded by these fimbrial operons. Motility is mediated by flagella encoded on a single contiguous 54-kb chromosomal sequence. On agar plates, P. mirabilis undergoes a morphological conversion to a filamentous swarmer cell expressing hundreds of flagella. When swarms from different strains meet, a line of demarcation, a "Dienes line," develops due to the killing action of each strain's type VI secretion system. During infection, histological damage is caused by cytotoxins including hemolysin and a variety of proteases, some autotransported. The pathogenesis of infection, including assessment of individual genes or global screens for virulence or fitness factors has been assessed in murine models of ascending urinary tract infections or CAUTIs using both single-species and polymicrobial models. Global gene expression studies performed in culture and in the murine model have revealed the unique metabolism of this bacterium. Vaccines, using MR/P fimbria and its adhesin, MrpH, have been shown to be efficacious in the murine model. A comprehensive review of factors associated with urinary tract infection is presented, encompassing both historical perspectives and current advances.

Rat Genome and Model Resources

Rats remain a major model for studying disease mechanisms and discovery, validation, and testing of new compounds to improve human health. The rat’s value continues to grow as indicated by the more than 1.4 million publications (second to human) at PubMed documenting important discoveries using this model. Advanced sequencing technologies, genome modification techniques, and the development of embryonic stem cell protocols ensure the rat remains an important mammalian model for disease studies. The 2004 release of the reference genome has been followed by the production of complete genomes for more than two dozen individual strains utilizing NextGen sequencing technologies; their analyses have identified over 80 million variants. This explosion in genomic data has been accompanied by the ability to selectively edit the rat genome, leading to hundreds of new strains through multiple technologies. A number of resources have been developed to provide investigators with access to precision rat models, comprehensive datasets, and sophisticated software tools necessary for their research. Those profiled here include the Rat Genome Database, PhenoGen, Gene Editing Rat Resource Center, Rat Resource and Research Center, and the National BioResource Project for the Rat in Japan.

The neXtProt knowledgebase on human proteins: 2017 update

The neXtProt human protein knowledgebase (https://www.nextprot.org) continues to add new content and tools, with a focus on proteomics and genetic variation data. neXtProt now has proteomics data for over 85% of the human proteins, as well as new tools tailored to the proteomics community.Moreover, the neXtProt release 2016-08-25 includes over 8000 phenotypic observations for over 4000 variations in a number of genes involved in hereditary cancers and channelopathies. These changes are presented in the current neXtProt update. All of the neXtProt data are available via our user interface and FTP site. We also provide an API access and a SPARQL endpoint for more technical applications.